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st: Re: xtreg, fe cluster(id) vs xtabond2 - just to control for serial correlation


From   "Rodrigo A. Alfaro" <[email protected]>
To   <[email protected]>
Subject   st: Re: xtreg, fe cluster(id) vs xtabond2 - just to control for serial correlation
Date   Fri, 15 Jun 2007 12:54:30 -0400

I just posted references for the same problem:
http://www.stata.com/statalist/archive/2007-06/msg00573.html

Shortcut: AB/GMM (xtabond) and Within-Groups or WG (xtreg, fe) are biased
for "large n and large T". In your case WG will be more biased that AB/GMM
if the panel is balanced, but your panel is unbalanced!!

Bruno (2004) shows that bias of WG will depend on the ratio between the
harmonic average of T (H) and the arithmetic avg of T (A): Ahrens- Pincus index
(see [5] at http://ideas.repec.org/e/pbr136.html). Maybe a good idea is to simulate
your case (n=4000) and (T=10) using -xtarsim-.

I don't know about AB/GMM in unbalanced panels. My guess is that also will
depend on H/A, but it seems hard to show that.

Without more information, I suggest to run both. I preference AB/GMM in
your case, just because T is not too large and because unbalancedness it could
be (in average) very-very low.

Rodrigo.
PS: The standard error corrections -cluster(id)- will not affect the estimator.


----- Original Message ----- From: "Alexander P�tz" <[email protected]>
To: <[email protected]>
Sent: Friday, June 15, 2007 12:29 PM
Subject: st: xtreg, fe cluster(id) vs xtabond2 - just to control for serial correlation



Dear Statalist members,

I've got a question concerning an unbalanced panel dataset with N about 4000
and T = 10.

I would like to regress a model with panel fixed effects and control for
serial correlation in the dependent variable. Now I'm not sure about
including the lagged dependent variable as an explanatory variable in the
model and estimating the equation by xtabond2 or just leaving the lagged
dependent variable out of the model and estimate the equation by xtreg, fe
cluster(id).

Because I'm not sure about how to use xtabond2 correctly and I'm only
interested in the signs and inference of the other explanatory variables, I
would tend to leave the lagged dependent out of the model. Anyway I don't
know if this would produce biased estimates.

Thank you for your help.

Alexander






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